Agent Honeypots: Using Honeypots to Defend Against AI Agent Insider Threats (Full Report Translation)

This article is a full translation of the arXiv report "Honeypots for AI Agent Insider Threats," which evaluates honeypots as a means of defending against AI agent insider threats. The report proposes three strategies: detection, attribution, and deterrence. Detection is used to alert when an agent exceeds its authority; its evidence is the most solid, and it can be piloted at low cost. Attribution is used to distinguish a one-off mistake from patterned misconduct; it still lacks production-environment evidence. Deterrence is used to reduce attack attempts; its evidence is the weakest, and it should be treated as a research hypothesis. The report cites Tracebit research stating that warning agents that honeypots may exist in the environment reduced the attack success rate from 20% to 3%, and that at the time there were actually no honeypots in the environment. The report also notes that honeypots can only cover paths where defenders have pre-placed decoys, making it difficult to address diffuse misconduct with high verification costs, and it gives five priority research directions: honeypot robustness, detection, attribution, deterrence, and supporting systems.